Identifying Tips Web Sites of a Specific Query based on Search Engine Suggests and the Topic Distribution

Abstract

This paper proposes techniques of automatically discovering tips Web sites from a large collection of Web pages using a topic model and support vector machine (SVM). Tips refer to practical knowledge or expertise that is used to help accomplish certain tasks in a particular field. We designed several approaches of extracting features with respect to domain names based on their distribution among Web pages and candidate tips Web sites. In addition, search engine suggests, the query keywords used to fetch Web pages from the search engine are also considered to present patterns that can be potential features. It was discovered from our dataset that domain names of tips Web sites (Web sites containing tips on a certain specific theme) are more likely dispersed among topics and Web pages. These domain names also tend to correspond to a larger number of search engine suggests. This paper verifies such observed patterns by training an SVM using those extracted features. Evaluation is performed in precision and recall to measure correctness of classifying whether or not a domain name belongs to a tips Web site.